127 lines
4.6 KiB
Markdown
127 lines
4.6 KiB
Markdown
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# CLVP
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## Overview
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The CLVP (Contrastive Language-Voice Pretrained Transformer) model was proposed in [Better speech synthesis through scaling](https://arxiv.org/abs/2305.07243) by James Betker.
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The abstract from the paper is the following:
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*In recent years, the field of image generation has been revolutionized by the application of autoregressive transformers and DDPMs. These approaches model the process of image generation as a step-wise probabilistic processes and leverage large amounts of compute and data to learn the image distribution. This methodology of improving performance need not be confined to images. This paper describes a way to apply advances in the image generative domain to speech synthesis. The result is TorToise - an expressive, multi-voice text-to-speech system.*
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This model was contributed by [Susnato Dhar](https://huggingface.co/susnato).
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The original code can be found [here](https://github.com/neonbjb/tortoise-tts).
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## Usage tips
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1. CLVP is an integral part of the Tortoise TTS model.
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2. CLVP can be used to compare different generated speech candidates with the provided text, and the best speech tokens are forwarded to the diffusion model.
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3. The use of the [`ClvpModelForConditionalGeneration.generate()`] method is strongly recommended for tortoise usage.
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4. Note that the CLVP model expects the audio to be sampled at 22.05 kHz contrary to other audio models which expects 16 kHz.
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## Brief Explanation:
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- The [`ClvpTokenizer`] tokenizes the text input, and the [`ClvpFeatureExtractor`] extracts the log mel-spectrogram from the desired audio.
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- [`ClvpConditioningEncoder`] takes those text tokens and audio representations and converts them into embeddings conditioned on the text and audio.
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- The [`ClvpForCausalLM`] uses those embeddings to generate multiple speech candidates.
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- Each speech candidate is passed through the speech encoder ([`ClvpEncoder`]) which converts them into a vector representation, and the text encoder ([`ClvpEncoder`]) converts the text tokens into the same latent space.
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- At the end, we compare each speech vector with the text vector to see which speech vector is most similar to the text vector.
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- [`ClvpModelForConditionalGeneration.generate()`] compresses all of the logic described above into a single method.
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Example :
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```python
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>>> import datasets
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>>> from transformers import ClvpProcessor, ClvpModelForConditionalGeneration
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>>> # Define the Text and Load the Audio (We are taking an audio example from HuggingFace Hub using `datasets` library).
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>>> text = "This is an example text."
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>>> ds = datasets.load_dataset("hf-internal-testing/librispeech_asr_dummy", "clean", split="validation")
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>>> ds = ds.cast_column("audio", datasets.Audio(sampling_rate=22050))
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>>> sample = ds[0]["audio"]
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>>> # Define processor and model.
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>>> processor = ClvpProcessor.from_pretrained("susnato/clvp_dev")
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>>> model = ClvpModelForConditionalGeneration.from_pretrained("susnato/clvp_dev")
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>>> # Generate processor output and model output.
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>>> processor_output = processor(raw_speech=sample["array"], sampling_rate=sample["sampling_rate"], text=text, return_tensors="pt")
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>>> generated_output = model.generate(**processor_output)
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```
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## ClvpConfig
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[[autodoc]] ClvpConfig
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- from_sub_model_configs
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## ClvpEncoderConfig
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[[autodoc]] ClvpEncoderConfig
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## ClvpDecoderConfig
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[[autodoc]] ClvpDecoderConfig
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## ClvpTokenizer
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[[autodoc]] ClvpTokenizer
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- save_vocabulary
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## ClvpFeatureExtractor
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[[autodoc]] ClvpFeatureExtractor
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- __call__
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## ClvpProcessor
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[[autodoc]] ClvpProcessor
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- __call__
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- decode
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- batch_decode
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## ClvpModelForConditionalGeneration
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[[autodoc]] ClvpModelForConditionalGeneration
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- forward
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- generate
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- get_text_features
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- get_speech_features
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## ClvpForCausalLM
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[[autodoc]] ClvpForCausalLM
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## ClvpModel
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[[autodoc]] ClvpModel
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## ClvpEncoder
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[[autodoc]] ClvpEncoder
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## ClvpDecoder
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[[autodoc]] ClvpDecoder
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